Assessing the predictive influence of cases in a state space process

Cavanaugh, Joseph E.

doi:10.1093/biomet/86.1.183

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…Extension to non-linear and non-Gaussian state space models is possible, but requires adopting a probabilistic perspective (Kitagawa, 1987;Carlin et al, 1992; Kitagawa, 1998). The development of diagnostics for state space models, which assess the observation influence (Cavanaugh and Johnson, 1999) as well as the observing array design, provide important extensions.…”

Section: Discussionmentioning

confidence: 99%

“…Here, it is expressed in terms of the smoother state rather than the predicted observations. The diagnostics (3) and (4) can be regarded as special cases of predictive influence functions (Johnson and Geisser, 1983;Cavanaugh and Johnson, 1999). We are also interested in a retrospective assessment of the sampling protocol, or the observing array design, used in the plankton monitoring program.…”

Section: Modelmentioning

confidence: 99%

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Interannual variability in a plankton time series

et al. 2003

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SUMMARYTemporal changes in a plankton time series are examined, with an emphasis on interannual variability. A stochastic cycle model is used which describes an annual cycle with a fixed frequency, but a randomly varying amplitude and phase. A state space representation is used with the Kalman filter, and associated fixed-interval smoother, to provide estimation of the time-varying state. Parameter estimation relies on maximum likelihood methods. A data set is considered comprised of an irregularly sampled time series of plankton (dinoflagellate) abundance over a 12 year period in the Bay of Fundy, off the east coast of Canada. Analysis of the log 10 -transformed data indicated timing changes in the seasonal cycle of up to 23 days. Significant variations in abundance relative to the mean cycle were found for some highly sampled summer periods. Case deletion diagnostics identified two influential observations, one of which has a large impact on the estimated system noise. Examination of the sampling protocol, or monitoring design, indicates the need to reduce the observation error variance in order to improve detection of interannual variations in plankton abundance.

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Section: Discussionmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Interannual variability in a plankton time series

et al. 2003

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“…Following Cavanaugh and Johnson (1999), we use an information theoretic approach to develop a disparity measure that gauges the similarity of two state-space processes by comparing posterior (conditional) densities of the unobserved states. (Based on these results, a measure targeting the marginal densities of the states is developed in the next subsection.…”

Section: A2 General State-space Divergence Measurementioning

confidence: 99%

State‐space discrimination and clustering of atmospheric time series data based on Kullback information measures

Bengtsson¹,

Cavanaugh

2007

Environmetrics

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SUMMARYStatistical problems in atmospheric science are frequently characterized by large spatio-temporal data sets and pose difficult challenges in classification and pattern recognition. Here, we consider the problem of identifying geographically homogeneous regions based on similarities in the temporal dynamics of weather patterns. Two disparity measures are proposed and applied to cluster time series of observed monthly temperatures from locations across Colorado, U.S.A. The two disparity measures are based on state-space models, where the monthly temperature anomaly dynamics and seasonal variation are represented by latent processes. Our disparity measures produce clusters consistent with known atmospheric flow structures. In particular, the temporal anomaly pattern is related to the topography of Colorado, where, separated by the Continental Divide, the flow structures in the western and eastern parts of the state have different dynamics. The results further suggest that seasonal variation may be affected by locally changing solar radiation levels primarily associated with elevation variations across the Rocky Mountains. The general methodology is outlined and developed in the Appendix. We conclude with a discussion of extensions to time varying and non-stationary systems.

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“…The K-L discrepancy has also been utilized to develop measures for diagnosing influential values in a data set. In the state-space framework, Cavanaugh and Johnson (1999) proposed a diagnostic based on the K-L discrepancy for the identification of cases that influence the recovery of the latent signal. Cavanaugh and Oleson (2001) generalized this diagnostic to the broader modeling problem of identifying cases that impact the recovery of missing or unobserved data.…”

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confidence: 99%

A unified discrepancy-based approach for balancing efficiency and robustness in state-space modeling estimation, selection, and diagnosis

Hu¹

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Assessing the predictive influence of cases in a state space process

Cited by 12 publications

References 26 publications

Interannual variability in a plankton time series

Interannual variability in a plankton time series

State‐space discrimination and clustering of atmospheric time series data based on Kullback information measures

A unified discrepancy-based approach for balancing efficiency and robustness in state-space modeling estimation, selection, and diagnosis

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